Overview

Dataset statistics

Number of variables13
Number of observations1139
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory115.8 KiB
Average record size in memory104.1 B

Variable types

Numeric13

Warnings

fixed acidity is highly correlated with citric acid and 2 other fieldsHigh correlation
volatile acidity is highly correlated with citric acidHigh correlation
citric acid is highly correlated with fixed acidity and 2 other fieldsHigh correlation
free sulfur dioxide is highly correlated with total sulfur dioxideHigh correlation
total sulfur dioxide is highly correlated with free sulfur dioxideHigh correlation
density is highly correlated with fixed acidity and 1 other fieldsHigh correlation
pH is highly correlated with fixed acidity and 1 other fieldsHigh correlation
alcohol is highly correlated with densityHigh correlation
fixed acidity is highly correlated with citric acid and 2 other fieldsHigh correlation
volatile acidity is highly correlated with citric acidHigh correlation
citric acid is highly correlated with fixed acidity and 2 other fieldsHigh correlation
free sulfur dioxide is highly correlated with total sulfur dioxideHigh correlation
total sulfur dioxide is highly correlated with free sulfur dioxideHigh correlation
density is highly correlated with fixed acidityHigh correlation
pH is highly correlated with fixed acidity and 1 other fieldsHigh correlation
alcohol is highly correlated with qualityHigh correlation
quality is highly correlated with alcoholHigh correlation
fixed acidity is highly correlated with pHHigh correlation
free sulfur dioxide is highly correlated with total sulfur dioxideHigh correlation
total sulfur dioxide is highly correlated with free sulfur dioxideHigh correlation
pH is highly correlated with fixed acidityHigh correlation
df_index is highly correlated with density and 1 other fieldsHigh correlation
free sulfur dioxide is highly correlated with total sulfur dioxideHigh correlation
sulphates is highly correlated with citric acid and 1 other fieldsHigh correlation
pH is highly correlated with density and 3 other fieldsHigh correlation
density is highly correlated with df_index and 3 other fieldsHigh correlation
fixed acidity is highly correlated with df_index and 4 other fieldsHigh correlation
citric acid is highly correlated with sulphates and 3 other fieldsHigh correlation
total sulfur dioxide is highly correlated with free sulfur dioxideHigh correlation
alcohol is highly correlated with pH and 2 other fieldsHigh correlation
chlorides is highly correlated with sulphates and 1 other fieldsHigh correlation
df_index has unique values Unique
citric acid has 106 (9.3%) zeros Zeros

Reproduction

Analysis started2021-05-22 10:57:58.603899
Analysis finished2021-05-22 10:58:52.109624
Duration53.51 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct1139
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean788.451273
Minimum1
Maximum1598
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2021-05-22T16:28:52.317566image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile66.9
Q1375.5
median773
Q31193.5
95-th percentile1528.1
Maximum1598
Range1597
Interquartile range (IQR)818

Descriptive statistics

Standard deviation470.5202595
Coefficient of variation (CV)0.5967651719
Kurtosis-1.22841138
Mean788.451273
Median Absolute Deviation (MAD)408
Skewness0.03707365356
Sum898046
Variance221389.3146
MonotonicityStrictly increasing
2021-05-22T16:28:52.584891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.1%
10641
 
0.1%
10731
 
0.1%
10721
 
0.1%
10701
 
0.1%
10691
 
0.1%
10661
 
0.1%
10651
 
0.1%
10631
 
0.1%
10761
 
0.1%
Other values (1129)1129
99.1%
ValueCountFrequency (%)
11
0.1%
21
0.1%
31
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
101
0.1%
121
0.1%
131
0.1%
ValueCountFrequency (%)
15981
0.1%
15971
0.1%
15951
0.1%
15941
0.1%
15931
0.1%
15911
0.1%
15901
0.1%
15891
0.1%
15881
0.1%
15871
0.1%

fixed acidity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct93
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.287884109
Minimum4.6
Maximum15.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2021-05-22T16:28:52.887285image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum4.6
5-th percentile6.1
Q17.1
median7.9
Q39.2
95-th percentile11.7
Maximum15.9
Range11.3
Interquartile range (IQR)2.1

Descriptive statistics

Standard deviation1.725695601
Coefficient of variation (CV)0.2082190796
Kurtosis0.9416591529
Mean8.287884109
Median Absolute Deviation (MAD)1
Skewness0.8895824737
Sum9439.9
Variance2.978025308
MonotonicityNot monotonic
2021-05-22T16:28:53.146286image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.844
 
3.9%
740
 
3.5%
7.636
 
3.2%
7.236
 
3.2%
7.135
 
3.1%
7.534
 
3.0%
7.934
 
3.0%
832
 
2.8%
7.731
 
2.7%
7.330
 
2.6%
Other values (83)787
69.1%
ValueCountFrequency (%)
4.61
 
0.1%
4.71
 
0.1%
4.91
 
0.1%
56
0.5%
5.14
0.4%
5.24
0.4%
5.34
0.4%
5.45
0.4%
5.51
 
0.1%
5.68
0.7%
ValueCountFrequency (%)
15.91
 
0.1%
15.62
0.2%
14.31
 
0.1%
141
 
0.1%
13.81
 
0.1%
13.51
 
0.1%
13.41
 
0.1%
13.33
0.3%
13.21
 
0.1%
131
 
0.1%

volatile acidity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct139
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5311720808
Minimum0.16
Maximum1.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2021-05-22T16:28:53.411009image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.16
5-th percentile0.27
Q10.39
median0.52
Q30.645
95-th percentile0.8705
Maximum1.58
Range1.42
Interquartile range (IQR)0.255

Descriptive statistics

Standard deviation0.1882786942
Coefficient of variation (CV)0.3544589427
Kurtosis1.240553462
Mean0.5311720808
Median Absolute Deviation (MAD)0.13
Skewness0.7776814335
Sum605.005
Variance0.03544886667
MonotonicityNot monotonic
2021-05-22T16:28:53.750026image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5834
 
3.0%
0.433
 
2.9%
0.529
 
2.5%
0.4229
 
2.5%
0.3827
 
2.4%
0.3927
 
2.4%
0.3126
 
2.3%
0.4925
 
2.2%
0.3424
 
2.1%
0.5624
 
2.1%
Other values (129)861
75.6%
ValueCountFrequency (%)
0.162
 
0.2%
0.185
0.4%
0.192
 
0.2%
0.23
 
0.3%
0.214
0.4%
0.224
0.4%
0.235
0.4%
0.249
0.8%
0.257
0.6%
0.269
0.8%
ValueCountFrequency (%)
1.581
 
0.1%
1.332
0.2%
1.241
 
0.1%
1.1851
 
0.1%
1.181
 
0.1%
1.131
 
0.1%
1.1151
 
0.1%
1.091
 
0.1%
1.071
 
0.1%
1.043
0.3%

citric acid
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct80
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.273476734
Minimum0
Maximum1
Zeros106
Zeros (%)9.3%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2021-05-22T16:28:54.300556image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.095
median0.26
Q30.43
95-th percentile0.6
Maximum1
Range1
Interquartile range (IQR)0.335

Descriptive statistics

Standard deviation0.1963704762
Coefficient of variation (CV)0.7180518552
Kurtosis-0.7937036894
Mean0.273476734
Median Absolute Deviation (MAD)0.17
Skewness0.304091174
Sum311.49
Variance0.03856136391
MonotonicityNot monotonic
2021-05-22T16:28:54.581556image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0106
 
9.3%
0.4950
 
4.4%
0.2433
 
2.9%
0.0831
 
2.7%
0.0229
 
2.5%
0.425
 
2.2%
0.124
 
2.1%
0.2623
 
2.0%
0.3122
 
1.9%
0.4222
 
1.9%
Other values (70)774
68.0%
ValueCountFrequency (%)
0106
9.3%
0.0117
 
1.5%
0.0229
 
2.5%
0.0318
 
1.6%
0.0419
 
1.7%
0.0516
 
1.4%
0.0616
 
1.4%
0.0713
 
1.1%
0.0831
 
2.7%
0.0920
 
1.8%
ValueCountFrequency (%)
11
 
0.1%
0.791
 
0.1%
0.781
 
0.1%
0.763
0.3%
0.751
 
0.1%
0.742
0.2%
0.731
 
0.1%
0.721
 
0.1%
0.711
 
0.1%
0.72
0.2%

residual sugar
Real number (ℝ≥0)

Distinct82
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.493634767
Minimum1.2
Maximum15.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2021-05-22T16:28:54.852726image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile1.6
Q11.9
median2.2
Q32.6
95-th percentile4.51
Maximum15.5
Range14.3
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation1.257423074
Coefficient of variation (CV)0.504253105
Kurtosis30.11981457
Mean2.493634767
Median Absolute Deviation (MAD)0.4
Skewness4.517630559
Sum2840.25
Variance1.581112787
MonotonicityNot monotonic
2021-05-22T16:28:55.108021image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2114
 
10.0%
2.289
 
7.8%
1.889
 
7.8%
2.183
 
7.3%
1.978
 
6.8%
2.565
 
5.7%
2.364
 
5.6%
2.663
 
5.5%
2.462
 
5.4%
1.654
 
4.7%
Other values (72)378
33.2%
ValueCountFrequency (%)
1.26
 
0.5%
1.35
 
0.4%
1.424
 
2.1%
1.521
 
1.8%
1.654
4.7%
1.652
 
0.2%
1.752
4.6%
1.752
 
0.2%
1.889
7.8%
1.978
6.8%
ValueCountFrequency (%)
15.51
 
0.1%
13.91
 
0.1%
13.41
 
0.1%
12.91
 
0.1%
10.71
 
0.1%
91
 
0.1%
8.91
 
0.1%
8.61
 
0.1%
8.33
0.3%
7.91
 
0.1%

chlorides
Real number (ℝ≥0)

HIGH CORRELATION

Distinct150
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08889727831
Minimum0.034
Maximum0.611
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2021-05-22T16:28:55.385892image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.034
5-th percentile0.052
Q10.069
median0.079
Q30.091
95-th percentile0.1521
Maximum0.611
Range0.577
Interquartile range (IQR)0.022

Descriptive statistics

Standard deviation0.05205890085
Coefficient of variation (CV)0.5856073643
Kurtosis35.43490675
Mean0.08889727831
Median Absolute Deviation (MAD)0.011
Skewness5.310015644
Sum101.254
Variance0.002710129158
MonotonicityNot monotonic
2021-05-22T16:28:55.639616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.07837
 
3.2%
0.0837
 
3.2%
0.07935
 
3.1%
0.08433
 
2.9%
0.07433
 
2.9%
0.07530
 
2.6%
0.08230
 
2.6%
0.0729
 
2.5%
0.07629
 
2.5%
0.08129
 
2.5%
Other values (140)817
71.7%
ValueCountFrequency (%)
0.0341
 
0.1%
0.0382
 
0.2%
0.0394
0.4%
0.0414
0.4%
0.0423
0.3%
0.0431
 
0.1%
0.0445
0.4%
0.0454
0.4%
0.0464
0.4%
0.0472
 
0.2%
ValueCountFrequency (%)
0.6111
0.1%
0.611
0.1%
0.4671
0.1%
0.4641
0.1%
0.4221
0.1%
0.4151
0.1%
0.4142
0.2%
0.4131
0.1%
0.4031
0.1%
0.4011
0.1%

free sulfur dioxide
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct58
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.92976295
Minimum1
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2021-05-22T16:28:55.924859image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q17
median14
Q321
95-th percentile35
Maximum72
Range71
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.39257462
Coefficient of variation (CV)0.6523998287
Kurtosis1.717221049
Mean15.92976295
Median Absolute Deviation (MAD)7
Skewness1.194146708
Sum18144
Variance108.0056072
MonotonicityNot monotonic
2021-05-22T16:28:56.178554image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6104
 
9.1%
572
 
6.3%
1255
 
4.8%
1554
 
4.7%
751
 
4.5%
1048
 
4.2%
948
 
4.2%
1646
 
4.0%
1341
 
3.6%
1741
 
3.6%
Other values (48)579
50.8%
ValueCountFrequency (%)
11
 
0.1%
21
 
0.1%
333
 
2.9%
428
 
2.5%
572
6.3%
5.51
 
0.1%
6104
9.1%
751
4.5%
839
 
3.4%
948
4.2%
ValueCountFrequency (%)
721
 
0.1%
661
 
0.1%
571
 
0.1%
541
 
0.1%
531
 
0.1%
523
0.3%
512
0.2%
502
0.2%
482
0.2%
471
 
0.1%

total sulfur dioxide
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct140
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.15276558
Minimum6
Maximum289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2021-05-22T16:28:56.474900image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q122
median38
Q363
95-th percentile114.1
Maximum289
Range283
Interquartile range (IQR)41

Descriptive statistics

Standard deviation33.99345304
Coefficient of variation (CV)0.7209217236
Kurtosis4.350239831
Mean47.15276558
Median Absolute Deviation (MAD)19
Skewness1.584630373
Sum53707
Variance1155.55485
MonotonicityNot monotonic
2021-05-22T16:28:56.737885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2428
 
2.5%
1427
 
2.4%
2827
 
2.4%
2027
 
2.4%
1925
 
2.2%
1223
 
2.0%
1622
 
1.9%
1322
 
1.9%
2621
 
1.8%
1821
 
1.8%
Other values (130)896
78.7%
ValueCountFrequency (%)
61
 
0.1%
74
 
0.4%
88
 
0.7%
912
1.1%
1019
1.7%
1118
1.6%
1223
2.0%
1322
1.9%
1427
2.4%
1521
1.8%
ValueCountFrequency (%)
2891
0.1%
2781
0.1%
1651
0.1%
1601
0.1%
1551
0.1%
1531
0.1%
1521
0.1%
1512
0.2%
1491
0.1%
1482
0.2%

density
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct400
Distinct (%)35.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9966482616
Minimum0.9902
Maximum1.0032
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2021-05-22T16:28:57.016901image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.9902
5-th percentile0.99354
Q10.99554
median0.99666
Q30.9978
95-th percentile0.9998
Maximum1.0032
Range0.013
Interquartile range (IQR)0.00226

Descriptive statistics

Standard deviation0.001834517257
Coefficient of variation (CV)0.001840686757
Kurtosis0.6544878003
Mean0.9966482616
Median Absolute Deviation (MAD)0.00114
Skewness0.0114171626
Sum1135.18237
Variance3.365453566 × 10-6
MonotonicityNot monotonic
2021-05-22T16:28:57.327456image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.996831
 
2.7%
0.99827
 
2.4%
0.997625
 
2.2%
0.997223
 
2.0%
0.996419
 
1.7%
0.998219
 
1.7%
0.996218
 
1.6%
0.99718
 
1.6%
0.997818
 
1.6%
0.996617
 
1.5%
Other values (390)924
81.1%
ValueCountFrequency (%)
0.99021
0.1%
0.99081
0.1%
0.990841
0.1%
0.99121
0.1%
0.99151
0.1%
0.991541
0.1%
0.991571
0.1%
0.991621
0.1%
0.99171
0.1%
0.991822
0.2%
ValueCountFrequency (%)
1.00321
 
0.1%
1.003151
 
0.1%
1.002891
 
0.1%
1.00262
 
0.2%
1.00181
 
0.1%
1.00144
0.4%
1.0016
0.5%
1.00083
0.3%
1.00064
0.4%
1.00045
0.4%

pH
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct87
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.308902546
Minimum2.74
Maximum4.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2021-05-22T16:28:57.609545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2.74
5-th percentile3.06
Q13.21
median3.31
Q33.4
95-th percentile3.57
Maximum4.01
Range1.27
Interquartile range (IQR)0.19

Descriptive statistics

Standard deviation0.1551792664
Coefficient of variation (CV)0.0468975028
Kurtosis0.9968547701
Mean3.308902546
Median Absolute Deviation (MAD)0.1
Skewness0.2736671599
Sum3768.84
Variance0.02408060473
MonotonicityNot monotonic
2021-05-22T16:28:57.890167image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.2637
 
3.2%
3.337
 
3.2%
3.3437
 
3.2%
3.3135
 
3.1%
3.3835
 
3.1%
3.3235
 
3.1%
3.3933
 
2.9%
3.2833
 
2.9%
3.3631
 
2.7%
3.2231
 
2.7%
Other values (77)795
69.8%
ValueCountFrequency (%)
2.741
 
0.1%
2.861
 
0.1%
2.871
 
0.1%
2.882
 
0.2%
2.91
 
0.1%
2.922
 
0.2%
2.931
 
0.1%
2.942
 
0.2%
2.951
 
0.1%
2.985
0.4%
ValueCountFrequency (%)
4.012
0.2%
3.92
0.2%
3.851
 
0.1%
3.782
0.2%
3.751
 
0.1%
3.741
 
0.1%
3.721
 
0.1%
3.712
0.2%
3.71
 
0.1%
3.683
0.3%

sulphates
Real number (ℝ≥0)

HIGH CORRELATION

Distinct93
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6593064091
Minimum0.33
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2021-05-22T16:28:58.178076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.33
5-th percentile0.48
Q10.55
median0.62
Q30.72
95-th percentile0.961
Maximum2
Range1.67
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.171697695
Coefficient of variation (CV)0.2604216987
Kurtosis10.24425283
Mean0.6593064091
Median Absolute Deviation (MAD)0.08
Skewness2.366662472
Sum750.95
Variance0.02948009847
MonotonicityNot monotonic
2021-05-22T16:28:58.455461image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5452
 
4.6%
0.648
 
4.2%
0.5847
 
4.1%
0.6246
 
4.0%
0.5644
 
3.9%
0.5341
 
3.6%
0.5741
 
3.6%
0.6137
 
3.2%
0.5937
 
3.2%
0.5535
 
3.1%
Other values (83)711
62.4%
ValueCountFrequency (%)
0.331
 
0.1%
0.372
 
0.2%
0.42
 
0.2%
0.423
 
0.3%
0.438
 
0.7%
0.448
 
0.7%
0.457
 
0.6%
0.4610
 
0.9%
0.4715
1.3%
0.4827
2.4%
ValueCountFrequency (%)
21
 
0.1%
1.981
 
0.1%
1.621
 
0.1%
1.611
 
0.1%
1.591
 
0.1%
1.561
 
0.1%
1.363
0.3%
1.341
 
0.1%
1.331
 
0.1%
1.311
 
0.1%

alcohol
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct63
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.44186421
Minimum8.4
Maximum14.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2021-05-22T16:28:58.760462image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum8.4
5-th percentile9.2
Q19.5
median10.2
Q311.2
95-th percentile12.5
Maximum14.9
Range6.5
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation1.099889774
Coefficient of variation (CV)0.1053346177
Kurtosis0.1194272189
Mean10.44186421
Median Absolute Deviation (MAD)0.8
Skewness0.8595552817
Sum11893.28333
Variance1.209757516
MonotonicityNot monotonic
2021-05-22T16:28:59.019462image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.586
 
7.6%
9.481
 
7.1%
9.258
 
5.1%
1055
 
4.8%
9.353
 
4.7%
9.849
 
4.3%
10.541
 
3.6%
9.740
 
3.5%
9.639
 
3.4%
10.238
 
3.3%
Other values (53)599
52.6%
ValueCountFrequency (%)
8.42
 
0.2%
8.51
 
0.1%
8.72
 
0.2%
913
 
1.1%
9.051
 
0.1%
9.119
 
1.7%
9.258
5.1%
9.2333333331
 
0.1%
9.251
 
0.1%
9.353
4.7%
ValueCountFrequency (%)
14.91
 
0.1%
145
0.4%
13.64
0.4%
13.566666671
 
0.1%
13.51
 
0.1%
13.43
0.3%
13.33
0.3%
13.21
 
0.1%
13.12
 
0.2%
134
0.4%

quality
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.608428446
Minimum3
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2021-05-22T16:28:59.245787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q15
median6
Q36
95-th percentile7
Maximum8
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8433337723
Coefficient of variation (CV)0.1503689992
Kurtosis0.3709331376
Mean5.608428446
Median Absolute Deviation (MAD)1
Skewness0.1631298923
Sum6388
Variance0.7112118514
MonotonicityNot monotonic
2021-05-22T16:28:59.444198image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5480
42.1%
6442
38.8%
7138
 
12.1%
453
 
4.7%
816
 
1.4%
310
 
0.9%
ValueCountFrequency (%)
310
 
0.9%
453
 
4.7%
5480
42.1%
6442
38.8%
7138
 
12.1%
816
 
1.4%
ValueCountFrequency (%)
816
 
1.4%
7138
 
12.1%
6442
38.8%
5480
42.1%
453
 
4.7%
310
 
0.9%

Interactions

2021-05-22T16:28:03.419242image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:03.733328image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:04.012442image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:04.307621image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:04.587679image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:04.876527image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:05.156931image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:05.444312image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:05.849357image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:06.132074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:06.419080image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:06.759122image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:07.099253image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:07.422980image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:07.737917image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:08.009026image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:08.270420image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:08.509424image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:08.780190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:09.023022image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:09.289203image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:09.544863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:09.806862image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:10.075288image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:10.333288image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:10.584839image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:10.833843image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:11.118561image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:11.380197image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:11.660959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:11.933192image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:12.234075image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:12.543732image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:12.828269image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:13.102911image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:13.383912image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:13.659912image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:13.946892image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:14.233542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:14.515923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:15.006599image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:15.255599image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:15.524061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:15.775592image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:16.045821image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:16.337883image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:16.593001image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:16.850609image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:17.132646image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:17.419015image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:17.700246image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:17.975784image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:18.228486image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:18.516487image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:18.808493image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:19.104073image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:19.387941image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:19.693799image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:19.968081image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:20.258873image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:20.526775image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:20.831937image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:21.153779image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:21.448923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:21.732870image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:22.008415image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:22.291267image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:22.558714image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:22.841915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:23.113940image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:23.396631image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:23.686589image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:23.983139image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:24.287427image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:24.554390image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:24.813220image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:25.080558image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:25.335549image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:25.584267image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:25.874844image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:26.310223image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:26.595022image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-05-22T16:28:27.147846image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-05-22T16:28:27.696136image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:27.972965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:28.254132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:28.517476image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:28.795660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:29.062729image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:29.319491image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:29.595597image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:29.847600image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:30.124176image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:30.388976image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:30.661439image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:30.918892image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:31.192048image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:31.455048image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:31.721691image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:31.987605image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:32.263006image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:32.525947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:32.776947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:33.061953image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:33.321642image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:33.599002image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:33.853919image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:34.131110image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:34.392758image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:34.667752image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:34.938753image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:35.213401image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:35.482771image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:35.766772image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:36.037772image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:36.304572image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:36.601389image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:36.852471image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:37.130285image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:37.389121image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:37.670989image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:37.932728image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:38.266684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:38.557841image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:38.895915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-05-22T16:28:39.629463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:39.891468image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:40.150826image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:40.443128image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:40.713932image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:41.002149image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:41.271148image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:41.574907image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:41.919927image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:42.251722image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:42.553622image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:42.856686image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:43.156676image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:43.433785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:43.733091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:44.012303image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:44.300714image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:44.576828image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:44.848626image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:45.120517image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:45.381521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:45.678324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:46.053158image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:46.391474image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:46.706579image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:46.970669image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:47.242670image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:47.510975image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:47.770603image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:48.043423image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:48.283513image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:48.537251image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:48.781051image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:49.044045image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:49.298119image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:49.556144image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:49.812616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:50.074775image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:50.328178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:50.587321image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-22T16:28:50.832839image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-05-22T16:28:59.675194image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-22T16:29:00.154850image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-22T16:29:00.616272image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-22T16:29:01.085839image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-05-22T16:28:51.311120image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-22T16:28:51.879394image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexfixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
017.80.8800.002.60.09825.067.00.99683.200.689.85
127.80.7600.042.30.09215.054.00.99703.260.659.85
2311.20.2800.561.90.07517.060.00.99803.160.589.86
357.40.6600.001.80.07513.040.00.99783.510.569.45
467.90.6000.061.60.06915.059.00.99643.300.469.45
577.30.6500.001.20.06515.021.00.99463.390.4710.07
687.80.5800.022.00.0739.018.00.99683.360.579.57
7106.70.5800.081.80.09715.065.00.99593.280.549.25
8125.60.6150.001.60.08916.059.00.99433.580.529.95
9137.80.6100.291.60.1149.029.00.99743.261.569.15

Last rows

df_indexfixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
112915875.80.6100.111.80.06618.028.00.994833.550.6610.96
113015887.20.6600.332.50.06834.0102.00.994143.270.7812.86
113115896.60.7250.207.80.07329.079.00.997703.290.549.25
113215906.30.5500.151.80.07726.035.00.993143.320.8211.66
113315915.40.7400.091.70.08916.026.00.994023.670.5611.66
113415936.80.6200.081.90.06828.038.00.996513.420.829.56
113515946.20.6000.082.00.09032.044.00.994903.450.5810.55
113615955.90.5500.102.20.06239.051.00.995123.520.7611.26
113715975.90.6450.122.00.07532.044.00.995473.570.7110.25
113815986.00.3100.473.60.06718.042.00.995493.390.6611.06